Reinforcement Learning for Intelligent Traffic Signal Control in Smart Cities
Article Sidebar
Main Article Content
Abstract
Traffic congestion is a major challenge in urban environments. This research explores the use of reinforcement learning algorithms to optimize traffic signal control dynamically. The proposed system learns traffic patterns and adjusts signal timings in real time to reduce congestion and waiting times. Simulation results demonstrate significant improvements in traffic flow and reduced emissions. The study highlights the potential of AI-driven traffic management systems in building smarter cities.
Article Details
References
Asopa, P., Purohit, P., Nadikattu, R. R., & Whig, P. (2021). Reducing carbon footprint for sustainable development of smart cities using IoT. In Proceedings of the Third International Conference on Intelligent Communication Technologies.
Whig, P., Velu, A., & Nadikattu, R. R. (2022). The economic impact of AI-enabled blockchain in 6G-based industry. In AI and blockchain technology in 6G wireless network (pp. 205–224).
Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain platform to resolve security issues in IoT and smart networks. In AI-enabled agile internet of things for sustainable FinTech ecosystems (pp. 46–65).
Vaddadi, S. A., Vallabhaneni, R., & Whig, P. (2023). Utilizing AI and machine learning in cybersecurity for sustainable development through enhanced threat detection and mitigation. International Journal of Sustainable Development Through AI, ML and IoT, 2(2).
Vemulapalli, G., Yalamati, S., Palakurti, N. R., Alam, N., Samayamantri, S., & Whig, P. (2024). Predicting obesity trends using machine learning from big data analytics approach. In Proceedings of the Asia Pacific Conference on Innovation in Technology (APCIT) (pp. 1–5).
Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (2024). Revolutionizing gender-specific healthcare: Harnessing deep learning for transformative solutions. In Transforming gender-based healthcare with AI and machine learning (pp. 14–26).